2,772 research outputs found
Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter
Microblogs are increasingly exploited for predicting prices and traded
volumes of stocks in financial markets. However, it has been demonstrated that
much of the content shared in microblogging platforms is created and publicized
by bots and spammers. Yet, the presence (or lack thereof) and the impact of
fake stock microblogs has never systematically been investigated before. Here,
we study 9M tweets related to stocks of the 5 main financial markets in the US.
By comparing tweets with financial data from Google Finance, we highlight
important characteristics of Twitter stock microblogs. More importantly, we
uncover a malicious practice - referred to as cashtag piggybacking -
perpetrated by coordinated groups of bots and likely aimed at promoting
low-value stocks by exploiting the popularity of high-value ones. Among the
findings of our study is that as much as 71% of the authors of suspicious
financial tweets are classified as bots by a state-of-the-art spambot detection
algorithm. Furthermore, 37% of them were suspended by Twitter a few months
after our investigation. Our results call for the adoption of spam and bot
detection techniques in all studies and applications that exploit
user-generated content for predicting the stock market
Universal, Unsupervised (Rule-Based), Uncovered Sentiment Analysis
We present a novel unsupervised approach for multilingual sentiment analysis
driven by compositional syntax-based rules. On the one hand, we exploit some of
the main advantages of unsupervised algorithms: (1) the interpretability of
their output, in contrast with most supervised models, which behave as a black
box and (2) their robustness across different corpora and domains. On the other
hand, by introducing the concept of compositional operations and exploiting
syntactic information in the form of universal dependencies, we tackle one of
their main drawbacks: their rigidity on data that are structured differently
depending on the language concerned. Experiments show an improvement both over
existing unsupervised methods, and over state-of-the-art supervised models when
evaluating outside their corpus of origin. Experiments also show how the same
compositional operations can be shared across languages. The system is
available at http://www.grupolys.org/software/UUUSA/Comment: 19 pages, 5 Tables, 6 Figures. This is the authors version of a work
that was accepted for publication in Knowledge-Based System
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